






"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							list[int]:
  lowerCamelCase_       	=		[0 for i in range(len(_lowerCamelCase					)					)]

  # initialize interval's left pointer and right pointer
  lowerCamelCase_	,      lowerCamelCase_       	=		0, 0

  for i in range(1						,							len(_lowerCamelCase					)					):
    # case when current index is inside the interval
    if i <= right_pointer:
      lowerCamelCase_       	=		min(right_pointer - i + 1						,							z_result[i - left_pointer]					)
      lowerCamelCase_       	=		min_edge

    while go_next(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					):
      z_result[i] += 1

    # if new index's result gives us more right interval,
    # we've to update left_pointer and right_pointer
    if i + z_result[i] - 1 > right_pointer:
      lowerCamelCase_	,      lowerCamelCase_       	=		i, i + z_result[i] - 1

  return z_result







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						list[int]						,							_lowerCamelCase			:						str					) ->							bool:
  return i + z_result[i] < len(_lowerCamelCase					) and s[z_result[i]] == s[i + z_result[i]]







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						str					) ->							int:
  lowerCamelCase_       	=		0
  # concatenate 'pattern' and 'input_str' and call z_function
  # with concatenated string
  lowerCamelCase_       	=		z_function(pattern + input_str					)

  for val in z_result:
    # if value is greater then length of the pattern string
    # that means this index is starting position of substring
    # which is equal to pattern string
    if val >= len(_lowerCamelCase					):
      answer += 1

  return answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()












"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




